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7 Pearson/Spearman's Correlation SPSS Tutorial

7 Pearson/Spearman's Correlation SPSS Tutorial

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deficiency were also positive for H. pylori infection (Hilu et al., 2015). They obtained

blood serum samples from patients who were deficient in vitamin B12 and ran an ELISA

to determine the levels of vitamin B12 and anti-H. pylori IgA.

*The



data used for this tutorial were taken from the study conducted by Hilu et al. (2015).

The data were simplified for display; however, the trend was kept.

Formulate a question about the data that can be addressed by performing a

Pearson Correlation.

Question: Is there a relationship between levels of anti-H. pylori IgA and vitamin

B12?

Based on the question, formulate the null and alternative hypotheses that

address the question proposed.

Null Hypothesis (H0): There is no relationship between levels of anti-H. pylori IgA

and vitamin B12.

Alternative Hypothesis (H1): There is a relationship between levels of anti-H. pylori

IgA and vitamin B12.

Now that an appropriate testable question has been developed along with a set of testable

hypotheses, you can run the statistical analysis.

Utilize the following tutorial to run Pearson or Spearman's correlation in SPSS.

Refer to Chapter 13 for getting started and understanding SPSS.

Check all assumptions prior to running the test.



Pearson's and Spearman's Correlation SPSS Tutorial

1. Your data should look similar to the following.



2. You can now run Pearson's correlation. To begin your analysis, click on the Analyze

tab in the toolbar located along the top of the page. Scroll down to the Correlate

option and select Bivariate.



3. The following screen will appear.



4. Click on the variable (IgA) that appears in the box to the left. Then click on the

corresponding arrow to move the variable over to the Variables list.



5. Next click on the variable remaining in the box to the left (vB12) and click the

corresponding arrow to move the variable over to the Variables list.



6. Check the Pearson box under Correlation Coefficients and Two-tailed under

Test of Significance.



Note: To run Spearman's correlation, simply click Spearman.



1. Click OK.



2. A separate document will appear. This is referred to as the output.



Pearson's correlation yielded a p-value = 0.001. A p-value less than the critical value

(0.05) indicates a significant correlation. Therefore, a relationship exists between vitamin

B12 deficiency and anti-H. pylori IgA (p = 0.001); therefore, we can reject the null

hypothesis and fail to reject the alternative hypothesis. The results also indicate that

r = −0.690 (a negative value), which demonstrates a negative relationship between the

two variables. In other words, the greater the infection (H. pylori), the lesser vitamin B12

is absorbed into the body.



Concluding Statement

There is a weak negative correlation (r = −0.690, p = 0.001) between the levels of anti-H.

pylori IgA and vitamin B12.

Note: If you want to save the SPSS file with the inserted data as well as the SPSS output

with the results of the statistical analysis performed, then you must save each document

separately (see Chapter 13).



10.8 Pearson/Spearman's Correlation R Tutorial

Sexual selection in primates differs among groups. Primates with a more competitive

social structure, such as chimpanzees who live in multi-male/multi-female groups, have a

large testicular volume (measured in cubic centimeters) in relation to body mass (kg).

Larger testicles in chimpanzees allow for sperm competition; chimpanzee males can



produce enough sperm to compete with other males’ sperm when females copulate with

two males in close succession. The more sperm a male produces, the greater the

likelihood his sperm will fertilize the ovum.

However, monogamous groups, like Hamadryas baboons, are usually associated with

smaller testicular volume in relation to body mass, as they do not need to regularly

participate in sperm competition for successful production of offspring. When comparing

primate testicular volume across species, it is often done so in conjunction with body

mass in order to make the testicular volumes comparable across species of different sizes.

Before utilizing the measures together, it is best to run a correlation to verify if there is a

relationship between testicular volume and body size. Usually, age is also taken into

account to ensure males are at the age of reproduction when testing correlation

hypotheses between testicular volume and body mass.

*The



data used for this tutorial were taken from the study conducted by Jolly and PhillipsConroy (2003).

Formulate a question about the data that can be addressed by performing a

correlation.

Question: Are testicular volume and body mass of Hamadryas baboons associated

with one another?

Based on the question, formulate the null and alternative hypotheses that

address the question proposed.

Null Hypothesis (H0): There is no relationship between Hamadryas baboon's

testicular volume and body mass.

Alternative Hypothesis (H1): There is a relationship between Hamadryas baboon's

testicular volume and body mass.

Now that an appropriate testable question has been developed along with a set of testable

hypotheses, you can run the statistical analysis.

This tutorial focuses on running correlations in R.

Refer to Chapter 15 for R-specific terminology and instructions on how to invoke and

construct code.

Check all assumptions prior to running the test.



Pearson's and Spearman's Correlation R Tutorial



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